In this contribution, we propose to use road and lane information as contextual cues in order to increase the precision of multi-object object tracking. For tracking, we employ a Monte Carlo implementation of a Probability Hypothesis Density (PHD)-filter, whereas scene context (road and lane information) is taken from annotated street maps. The novel aspect of the presented work is the tightly coupling of context information and the particle filtering process. This is achieved by injecting a priori particles representing locally expected motions, which are in turn determined by the local road and the lane configuration. This approach is evaluated on objects (tracklets) from the public KITTI benchmark database. Our experimental findings demonstrate a considerable tracking precision increasing when including this kind of a priori knowledge. At the same time, the approach is able to determine objects whose movements differ from the locally expected motion, which is an important feature for safety applications.